{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center><h2>Testing the Functionality of the <i>\"utils.py\"</i> Module</h2></center>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "' Tetsing the following functionality: \\n    * metrics (rmsd, gdt_ts, gdt_ha, tmscore)\\n    * alignment (kabsch)\\n    * 3d coords (mds)\\n    -----\\n    The data files used contain the id of the original\\n    crystal structures from the RCSB PDB\\n'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\" Tetsing the following functionality: \n",
    "    * metrics (rmsd, gdt_ts, gdt_ha, tmscore)\n",
    "    * alignment (kabsch)\n",
    "    * 3d coords (mds)\n",
    "    -----\n",
    "    The data files used contain the id of the original\n",
    "    crystal structures from the RCSB PDB\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import sys\n",
    "# science\n",
    "import numpy as np\n",
    "import torch\n",
    "import matplotlib.pyplot as plt\n",
    "# molecular utils\n",
    "import mdtraj\n",
    "# functionality\n",
    "sys.path.append(\"../\")\n",
    "from alphafold2_pytorch.utils import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load pdb file - has 1 more N_term than it should\n",
    "prot = mdtraj.load_pdb(\"data/1h22_protein_chain_1.pdb\").xyz[0].transpose()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# alter a small amount and measure metrics\n",
    "pred = prot + (2*np.random.rand(*prot.shape) - 1) * 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rmsd is:  [0.57793009]\n",
      "gdt_ha is:  [0.64619016]\n",
      "gdt_ts is:  [0.88010789]\n",
      "tm_score is:  [0.9980016]\n",
      "rmsd is:  tensor([0.5779], dtype=torch.float64)\n",
      "gdt_ha is:  tensor([0.6462])\n",
      "gdt_ts is:  tensor([0.8801])\n",
      "tm_score is:  tensor([0.9980], dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Numpy\n",
    "rmsd     = RMSD(prot, pred)\n",
    "gdt_ha   = GDT(prot, pred, mode=\"HA\")\n",
    "gdt_ts   = GDT(prot, pred, mode=\"TS\")\n",
    "tm_score = TMscore(prot, pred)\n",
    "\n",
    "print(\"rmsd is: \", rmsd)\n",
    "print(\"gdt_ha is: \", gdt_ha)\n",
    "print(\"gdt_ts is: \", gdt_ts)\n",
    "print(\"tm_score is: \", tm_score)\n",
    "\n",
    "# Torch\n",
    "prot, pred = torch.tensor(prot), torch.tensor(pred)\n",
    "rmsd     = RMSD(prot, pred)\n",
    "gdt_ha   = GDT(prot, pred, mode=\"HA\")\n",
    "gdt_ts   = GDT(prot, pred, mode=\"TS\")\n",
    "tm_score = TMscore(prot, pred)\n",
    "\n",
    "print(\"rmsd is: \", rmsd)\n",
    "print(\"gdt_ha is: \", gdt_ha)\n",
    "print(\"gdt_ts is: \", gdt_ts)\n",
    "print(\"tm_score is: \", tm_score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Alignment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "prot = prot.cpu().numpy()\n",
    "pred = pred.cpu().numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# rotation matrix\n",
    "R = np.array([[0.25581, -0.77351, 0.57986],\n",
    "              [-0.85333, -0.46255, -0.24057],\n",
    "              [0.45429, -0.43327, -0.77839]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# perturb protein (translation + rotation + random)\n",
    "pred = prot + (2*np.random.rand(*prot.shape) - 1) * 1 \n",
    "pred = np.dot(R, pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(tensor(0.5786, dtype=torch.float64), tensor(0.1677, dtype=torch.float64))"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check realignment works - torch\n",
    "pred_mod_, prot_mod_ = kabsch_torch(torch.tensor(pred).double(), torch.tensor(prot).double())\n",
    "rmsd_torch(prot_mod_, pred_mod_), tmscore_torch(prot_mod_, pred_mod_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.5786325232948007, 0.16767657305887732)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check realignment works - numpy\n",
    "pred_mod, prot_mod = kabsch_numpy(pred, prot)\n",
    "rmsd_numpy(prot_mod, pred_mod), tmscore_numpy(prot_mod, pred_mod)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3d Converter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "prot_traj = mdtraj.load_pdb(\"data/1h22_protein_chain_1.pdb\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "prot = prot_traj.xyz[0].transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# works with a simple distance matrix for now\n",
    "prot = torch.tensor(prot)\n",
    "dist_mat = torch.cdist(prot.t(), prot.t())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plt.imshow(distogram, cmap=\"viridis_r\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(torch.Size([1, 482]), torch.Size([1, 482]), torch.Size([1, 482]))"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# select indices of backbone for angle calculation and selection\n",
    "N_mask  = torch.tensor( prot_traj.topology.select(\"name == N and backbone\") ).unsqueeze(0)\n",
    "CA_mask = torch.tensor( prot_traj.topology.select(\"name == CA and backbone\") ).unsqueeze(0) \n",
    "C_mask  = torch.tensor( prot_traj.topology.select(\"name == C and backbone\")  ).unsqueeze(0)\n",
    "CA_mask.shape, N_mask.shape, C_mask.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Corrected mirror idxs: tensor([0, 2, 4])\n"
     ]
    }
   ],
   "source": [
    "sample = torch.cat([dist_mat.cpu().unsqueeze(0)]*5, dim=0)\n",
    "sample.requires_grad = True\n",
    "preds, stresses = MDScaling(sample,\n",
    "                            iters=3, tol=1e-5, fix_mirror=1, eigen=True,\n",
    "                            N_mask=N_mask, CA_mask=CA_mask, C_mask=C_mask, verbose=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "it: 0, stress tensor([0.0070])\n",
      "it: 1, stress tensor([0.0068])\n",
      "breaking at iteration 1 with stress tensor([3.5945e-05])\n",
      "Corrected mirror idxs: tensor([], dtype=torch.int64)\n"
     ]
    }
   ],
   "source": [
    "preds, stresses = MDScaling(torch.cat([dist_mat.cpu().unsqueeze(0)]*1, dim=0),\n",
    "                            iters=50, tol=1e-5, fix_mirror=1,\n",
    "                            N_mask=N_mask, CA_mask=CA_mask, C_mask=C_mask, verbose=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Corrected mirror idxs: tensor([], dtype=torch.int64)\n"
     ]
    }
   ],
   "source": [
    "preds, stresses = MDScaling(dist_mat.cpu(), iters=5, tol=1e-5, fix_mirror=1, eigen=True,\n",
    "                            N_mask=N_mask, CA_mask=CA_mask, C_mask=C_mask, verbose=2)\n",
    "pred, stress = preds[0], stresses[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rmsd is:  [8.0834025e-06]\n",
      "gdt_ha is:  [1.]\n",
      "gdt_ts is:  [1.]\n",
      "tm_score is:  [1.]\n"
     ]
    }
   ],
   "source": [
    "# check realignment works\n",
    "pred_mod, prot_mod = Kabsch(pred.numpy(), prot.numpy())\n",
    "# measure\n",
    "rmsd     = RMSD(prot_mod, pred_mod)\n",
    "gdt_ha   = GDT(prot_mod, pred_mod, mode=\"HA\")\n",
    "gdt_ts   = GDT(prot_mod, pred_mod, mode=\"TS\")\n",
    "tm_score = TMscore(prot_mod, pred_mod)\n",
    "\n",
    "print(\"rmsd is: \", rmsd)\n",
    "print(\"gdt_ha is: \", gdt_ha)\n",
    "print(\"gdt_ts is: \", gdt_ts)\n",
    "print(\"tm_score is: \", tm_score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## See reconstruction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_dist_mat = torch.cdist(pred.t(), pred.t())\n",
    "delta_dist   = new_dist_mat - dist_mat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Diffs: max = 0.0027621358167380095 and min -0.005524271633476019\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 648x216 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,3,figsize=(9, 3), sharey=True)\n",
    "cmap = plt.get_cmap(\"viridis_r\")\n",
    "\n",
    "ax[0].set_title(\"Original\")\n",
    "ax[0].imshow(dist_mat, cmap=\"viridis_r\")\n",
    "ax[1].set_title(\"Reconstructed\")\n",
    "ax[1].imshow(new_dist_mat, cmap=\"viridis_r\")\n",
    "ax[2].set_title(\"Difference\")\n",
    "ax[2].imshow(delta_dist, cmap=\"viridis_r\")\n",
    "\n",
    "\n",
    "print(\"Diffs: max = {0} and min {1}\".format(np.amax(delta_dist.numpy()),\n",
    "                                            np.amin(delta_dist.numpy()) ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save pdb file and check manually here: \n",
    "# https://molstar.org/viewer/\n",
    "buffer_save = mdtraj.load(\"data/1h22_protein_chain_1.pdb\")\n",
    "buffer_save.xyz = pred_mod.T[None, :, :]\n",
    "buffer_save.save(\"data/save_to_check.pdb\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save pdb file and check manually here: \n",
    "# https://molstar.org/viewer/\n",
    "buffer_save = mdtraj.load(\"data/1h22_protein_chain_1.pdb\")\n",
    "buffer_save.xyz = prot_mod.T[None, :, :]\n",
    "buffer_save.save(\"data/save_to_check_base.pdb\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Legend:\n",
    "* blue/purple: reference\n",
    "* green/orange: our reconstruction\n",
    "\n",
    "Comments: only small blue detail on the left. increase match by decreasing tolerance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"data/comparison.png\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Julia 1.5.3",
   "language": "julia",
   "name": "julia-1.5"
  },
  "language_info": {
   "file_extension": ".jl",
   "mimetype": "application/julia",
   "name": "julia",
   "version": "1.5.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
